Using paired but unlabeled MRI scans of the same patient, a consistency-based adaptation method improved white matter lesion segmentation on a new scanner domain compared with adversarial and mean-teacher baselines.
Multi-Domain Adversarial Learning
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abstract
Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias. This paper presents a multi-domain adversarial learning approach, MuLANN, to leverage multiple datasets with overlapping but distinct class sets, in a semi-supervised setting. Our contributions include: i) a bound on the average- and worst-domain risk in MDL, obtained using the H-divergence; ii) a new loss to accommodate semi-supervised multi-domain learning and domain adaptation; iii) the experimental validation of the approach, improving on the state of the art on two standard image benchmarks, and a novel bioimage dataset, Cell.
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Multi-Domain Adaptation in Brain MRI through Paired Consistency and Adversarial Learning
Using paired but unlabeled MRI scans of the same patient, a consistency-based adaptation method improved white matter lesion segmentation on a new scanner domain compared with adversarial and mean-teacher baselines.